Various works have been proposed for low-power KWS. Zhang et al.[1]} implements several different KWS models on micro controllers to compare their accuracy and memory/compute requirements. Zheng et al.[2]} proposes a binary neural network-based design with on-chip self-learning to update the entire model. Dbouk et al.[3]} uses recurrent attention network and hybrid digital and multibit in-memory computing for ultra low power KWS. Guo et al.[4]} proposes hybrid digital circuits and 16 64x64 SRAM-based in-memory computing (IMC) macros with 3-bit ADC for recurrent neural network-based KWS. Liu et al.[5]} uses precision self-adaptive computing for a binary weight network to reduce power, and [6]} uses mixed mode computing for low-power KWS. In summary, they reduce power consumption through recurrent models, quantized/binary neural network models, or voice activity detection. However, their model design did not consider the underlying hardware constraints or non-ideal effects for IMC.
